Spin-images have been widely used for surface registration
and object detection from range images in that they are scale, rotation,
and pose invariant. The computational complexity, however, is linear to
the number of spin images in the model data set because valid candidates
are chosen according to the similarity distribution between the input spin
image and whole spin images in the data set. In this paper we present
a fast method for valid candidate selection as well as approximate esti-
mate of the similarity distribution using outlier search in the partitioned
vocabulary trees. The sampled spin images in each tree are used for
approximate density estimation and best matched candidates are then
collected in the trees according to the statistics of the density. In contrast
to the previous approaches that attempt to build compact representa-
tions of the spin images, the proposed method reduces the search space
using the hierarchical clusters of the spin images such that the computa-
tional complexity is drastically reduced from O(K N) to O(K logN).
K and N are the size of the spin-image features and the model data sets
respectively. As demonstrated in the experimental results with a con-
sumer depth camera, the proposed method is tens of times faster than
the conventional method while the registration accuracy is preserved.